Systems | Development | Analytics | API | Testing

WSO2 APK (WSO2 API Platform for Kubernetes) provides a robust, Kubernetes-native platform for managing APIs. It’s purpose-built for cloud-native teams requiring fine-grained control over APIs in modern, distributed environments. With support for microservices architecture, secure ingress, and service discovery, APK solves the infrastructure side of the API equation.

In a world where data-driven decisions shape the future of every business, ETL (Extract, Transform, Load) processes are the backbone of operational intelligence. For organizations using Microsoft SQL Server, optimizing ETL pipelines isn't just a technical choice—it’s a strategic imperative. With over two decades in the ETL trenches, I’ve seen what works, what fails, and what silently erodes performance behind the scenes.

For many organizations across industries, the era of experimental AI has given way to the era of practical implementation. Even those companies still testing and evaluating AI solutions are shifting away from the art of the possible to focus more closely on what will soon produce measurable ROI. “It will no longer be enough for your organization to merely use AI to win the approval of company leadership,” says Samuel Lee, Product Marketing Director for Financial Services at Snowflake.

Data storage has been evolving, from databases to data warehouses and expansive data lakes, with each architecture responding to different business and data needs. Traditional databases excelled at structured data and transactional workloads but struggled with performance at scale as data volumes grew. The data warehouse solved for performance and scale but, much like the databases that preceded it, relied on proprietary formats to build vertically integrated systems.

BigQuery data preparation is generally available and integrates with BigQuery pipelines and BigQuery repositories and Git.

Adopting AI? Risky. Not adopting AI? Riskier… It’s an interesting conundrum illustrating a broader truth: playing it safe is sometimes risky. To succeed, companies need to take smart risks—and those who do often achieve the greatest rewards. But a risk that is smart for one organization may not be as smart for another, so what risk is right for you?

Pharmaceutical labeling is an ideal use case for AI because it’s a complex process that requires high levels of accuracy. Inaccurate labeling can result in: With recent breakthroughs in AI technology, pharmaceutical companies have rushed to explore its potential. But many have not seen the impact they expected. The problem isn’t the AI. It’s how pharma companies are using AI.

Millions of users rushed to ChatGPT overnight, all craving Studio Ghibli-style art. What started as a fun trend quickly went viral, pushing OpenAI’s servers to their limits. The "Ghibli Trend" wasn't just another online craze — it became a live performance and load-testing scenario for OpenAI. Social media users began sharing Ghibli-inspired AI images, creating a massive buzz.

Despite fears of job loss, QA professionals are leaning into AI faster than ever, according to Katalon’s newly released 2025 State of Software Quality Report. The report reveals that testers using AI tools are twice as likely to fear being replaced by them, a paradox that underscores the profession’s evolving relationship with automation.

Teams often find themselves managing a big number of test cases, many of which are nearly identical except for variations in input data. This redundancy leads to a higher risk of missing critical test coverage. Test parameterization solves this challenge by allowing testers to re-run the same test logic with multiple data iterations - teams define parameters within a single test script and execute it dynamically via a dataset with different values.